An efficient wavelet-based deblocking algorithm for highly compressed images

In this paper, a new post-processing method is proposed in the wavelet domain for the suppression of blocking artifacts in compressed images. The novelty of our method is that we can obtain soft-threshold values based on the difference between the wavelet transform coefficients of image blocks and the coefficients of the entire image to threshold high-frequency wavelet coefficients in different subbands using different values and strategies. The threshold value is made adaptive to different images and characteristics of blocking artifacts. In particular, the new method is robust, fast, and works remarkably well for different discrete-cosine-transform-based compressed images at low bit rates. The method is nonlinear, computationally efficient, and spatially adaptive. Another advantage of the new method is that it retains sharp features in the images since it only removes artifacts. Experimental results show that the proposed method can achieve a significantly improved visual quality and also increase the PSNR in the output image. Our method also performs better than algorithms based on overcomplete wavelet presentation for images containing a large portion of texture.